Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn

last summer my family and i visited russia even though none of us could read russian we did not have any trouble in figuring our way out all thanks to google's real-time translation of russian boards into english this is just one of the several applications of neural networks neural networks form the base of deep learning a subfield of machine learning where the algorithms are inspired by the structure of the human brain neural networks take in data train themselves to recognize the patterns in this data and then predict the outputs for a new set of similar data let's understand how this is done let's construct a neural network that differentiates between a square circle and triangle neural networks are made up of layers of neurons these neurons are the core processing units of the network first we have the input layer which receives the input the output layer predicts our final output in between exists the hidden layers which perform most of the computations required by our network here's an image of a circle this image is composed of 28 by 28 pixels which make up for 784 pixels each pixel is fed as input to each neuron of the first layer neurons of one layer are connected to neurons of the next layer through channels each of these channels is assigned a numerical value known as weight the inputs are multiplied to the corresponding weights and their sum is sent as input to the neurons in the hidden layer each of these neurons is associated with a numerical value called the bias which is then added to the input sum this value is then passed through a threshold function called the activation function the result of the activation function determines if the particular neuron will get activated or not an activated neuron transmits data to the neurons of the next layer over the channels in this manner the data is propagated through the network this is called forward propagation in the output layer the neuron with the highest value fires and determines the output the values are basically a probability for example here our neuron associated with square has the highest probability hence that's the output predicted by the neural network of course just by a look at it we know our neural network has made a wrong prediction but how does the network figure this out note that our network is yet to be trained during this training process along with the input our network also has the output fed to it the predicted output is compared against the actual output to realize the error in prediction the magnitude of the error indicates how wrong we are and the sign suggests if our predicted values are higher or lower than expected the arrows here give an indication of the direction and magnitude of change to reduce the error this information is then transferred backward through our network this is known as back propagation now based on this information the weights are adjusted this cycle of forward propagation and back propagation is iteratively performed with multiple inputs this process continues until our weights are assigned such that the network can predict the shapes correctly in most of the cases this brings our training process to an end you might wonder how long this training process takes honestly neural networks may take hours or even months to train but time is a reasonable trade-off when compared to its scope let us look at some of the prime applications of neural networks facial recognition cameras on smartphones these days can estimate the age of the person based on their facial features this is neural networks at play first differentiating the face from the background and then correlating the lines and spots on your face to a possible age forecasting neural networks are trained to understand the patterns and detect the possibility of rainfall or rise in stock prices with high accuracy music composition neural networks can even learn patterns in music and train itself enough to compose a fresh tune so here's a question for you which of the following statements does not hold true a activation functions are threshold functions b error is calculated at each layer of the neural network c both forward and back propagation take place during the training process of a neural network d most of the data processing is carried out in the hidden layers leave your answers in the comments section below three of you stand a chance to win amazon vouchers so don't miss it with deep learning and neural networks we are still taking baby steps the growth in this field has been foreseen by the big names companies such as google amazon and nvidia have invested in developing products such as libraries predictive models and intuitive gpus that support the implementation of neural networks the question dividing the visionaries is on the reach of neural networks to what extent can we replicate the human brain we'd have to wait a few more years to give a definite answer but if you enjoyed this video it would only take a few seconds to 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Channel: Simplilearn

Views: 1,371,906

Keywords: neural network, what is neural network, what is neural network in artificial intelligence, what is neural network in ai, how neural networks work, how deep neural networks work, introduction to neural networks, introduction to neural network in artificial intelligence, neural network tutorial, deep learning and neural networks, neural network explained, neural network explained easily, neural network in 5 minutes, deep learning, simplilearn deep learning, simplilearn

Id: bfmFfD2RIcg

Length: 5min 45sec (345 seconds)

Published: Wed Jun 19 2019